1. Technical Field
The invention relates to a method and computer program product for producing an optimal classifier model for a pattern recognition system. Image processing systems often contain pattern recognition systems.
2. Description of the Prior Art
Pattern recognition systems, loosely defined, are systems capable of distinguishing between various classes of real world stimuli according to their divergent characteristics. A number of applications require pattern recognition systems, which allow a system to deal with unrefined data without significant human intervention. By way of example, a pattern recognition system may attempt to classify individual letters to reduce a handwritten document to electronic text. Alternatively, the system may classify spoken utterances to allow verbal commands to be received at a computer console.
A typical prior art classifier is trained over a plurality of output classes using a set of training samples. The training samples are processed, data relating to features of interest are extracted, and training parameters are derived from this feature data. As the system receives an input associated with one of a plurality of classes, it analyzes its relationship to each class via a classification technique based upon these training parameters. From this analysis, the system produces an output class and an associated confidence value.
Designing a pattern recognition classifier for a specific application is generally a hit or miss task. It often requires multiple iterations and a significant amount of time to develop a working system. Further, in some applications, new output classes frequently appear in the population of samples classified by the system. For these applications, it is necessary to regularly add new output classes to reflect changes in the data population. Without some method of identifying new classes, the system will not be able to deal with the novel patterns effectively. Once discovered, it will also be necessary to retrain the classifier to accept the new class. To maintain optimal system performance, it may be necessary to reconfigure the design of the classifier in light of the newly added output classes, requiring another lengthy training process.